{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "%matplotlib inline"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "特别的，对于图像任务，我们创建了一个包 torchvision，它包含了处理一些基本图像数据集的方法。这些数据集包括 Imagenet, CIFAR10, MNIST 等。除了数据加载以外，torchvision 还包含了图像转换器， torchvision.datasets 和 torch.utils.data.DataLoader。\n",
    "\n",
    "torchvision包不仅提供了巨大的便利，也避免了代码的重复\n",
    "训练一个图像分类器\n",
    "依次按照下列顺序进行：\n",
    "\n",
    "使用torchvision加载和归一化CIFAR10训练集和测试集\n",
    "定义一个卷积神经网络\n",
    "定义损失函数\n",
    "在训练集上训练网络\n",
    "在测试集上测试网络"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5, 0.5, 0.5), (0.5,0.5,0.5))]  #torchvision的输出是[0,1]的PILImage图像，我们把它转换为归一化范围为[-1, 1]的张量。\n",
    ")\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True, \n",
    "                                                                                    download=True,transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset,batch_size = 4,\n",
    "shuffle=True,num_workers=4)\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,download=True,transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset,batch_size=4,shuffle=False,num_workers=4)\n",
    "classes = ('plane', 'car', 'bird', 'cat','deer','dog','frog','horse','ship','truck')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "def imshow(img):\n",
    "    img = img /2 +0.5\n",
    "    npimg =img.numpy()\n",
    "    plt.imshow(np.transpose(npimg,(1,2,0)))\n",
    "\n",
    "dataiter = iter(trainloader) #将可迭代对象转成迭代器\n",
    "images, labels = dataiter.next()\n",
    "\n",
    "imshow(torchvision.utils.make_grid(images))\n",
    "print(' '.join('%5s'% classes[labels[j]] for j in range(4)))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      " bird   dog  bird truck\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "class Net(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3,6,5)\n",
    "        self.pool=nn.MaxPool2d(2,2)\n",
    "        self.conv2 = nn.Conv2d(6,16,5)\n",
    "        self.fc1 = nn.Linear(16*5*5,120)\n",
    "        self.fc2 = nn.Linear(120,84)\n",
    "        self.fc3 = nn.Linear(84,10)\n",
    "    def forward(self,x):\n",
    "        x = self.pool(F.relu(self.conv1(x)))\n",
    "        x = self.pool(F.relu(self.conv2(x)))\n",
    "        x = x.view(-1,16*5*5)\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "net = Net().cuda()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "import torch.optim as optim\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.SGD(net.parameters(),lr=0.001,momentum=0.9)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "\n",
    "for epoch in range(100):  # 多批次循环\n",
    "\n",
    "    running_loss = 0.0\n",
    "    for i, data in enumerate(trainloader, 0):\n",
    "        # 获取输入\n",
    "        inputs, labels = data\n",
    "        inputs = inputs.cuda()\n",
    "        labels = labels.cuda()\n",
    "\n",
    "        # 梯度置0\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # 正向传播，反向传播，优化\n",
    "        outputs = net(inputs)\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # 打印状态信息\n",
    "        running_loss += loss.item()\n",
    "        if i % 2000 == 1999:    # 每2000批次打印一次\n",
    "            print('[%d, %5d] loss: %.3f' %\n",
    "                  (epoch + 1, i + 1, running_loss / 2000))\n",
    "            running_loss = 0.0\n",
    "\n",
    "print('Finished Training')"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1,  2000] loss: 1.090\n",
      "[1,  4000] loss: 1.105\n",
      "[1,  6000] loss: 1.086\n",
      "[1,  8000] loss: 1.102\n",
      "[1, 10000] loss: 1.114\n",
      "[1, 12000] loss: 1.083\n",
      "[2,  2000] loss: 1.027\n",
      "[2,  4000] loss: 0.995\n",
      "[2,  6000] loss: 1.032\n",
      "[2,  8000] loss: 1.031\n",
      "[2, 10000] loss: 1.031\n",
      "[2, 12000] loss: 1.015\n",
      "[3,  2000] loss: 0.945\n",
      "[3,  4000] loss: 0.955\n",
      "[3,  6000] loss: 0.948\n",
      "[3,  8000] loss: 0.981\n",
      "[3, 10000] loss: 0.989\n",
      "[3, 12000] loss: 0.997\n",
      "[4,  2000] loss: 0.901\n",
      "[4,  4000] loss: 0.888\n",
      "[4,  6000] loss: 0.926\n",
      "[4,  8000] loss: 0.937\n",
      "[4, 10000] loss: 0.935\n",
      "[4, 12000] loss: 0.951\n",
      "[5,  2000] loss: 0.831\n",
      "[5,  4000] loss: 0.895\n",
      "[5,  6000] loss: 0.882\n",
      "[5,  8000] loss: 0.891\n",
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      "[85,  8000] loss: 0.624\n",
      "[85, 10000] loss: 0.658\n",
      "[85, 12000] loss: 0.712\n",
      "[86,  2000] loss: 0.574\n",
      "[86,  4000] loss: 0.583\n",
      "[86,  6000] loss: 0.627\n",
      "[86,  8000] loss: 0.657\n",
      "[86, 10000] loss: 0.666\n",
      "[86, 12000] loss: 0.688\n",
      "[87,  2000] loss: 0.577\n",
      "[87,  4000] loss: 0.639\n",
      "[87,  6000] loss: 0.594\n",
      "[87,  8000] loss: 0.635\n",
      "[87, 10000] loss: 0.660\n",
      "[87, 12000] loss: 0.716\n",
      "[88,  2000] loss: 0.560\n",
      "[88,  4000] loss: 0.604\n",
      "[88,  6000] loss: 0.618\n",
      "[88,  8000] loss: 0.647\n",
      "[88, 10000] loss: 0.637\n",
      "[88, 12000] loss: 0.698\n",
      "[89,  2000] loss: 0.627\n",
      "[89,  4000] loss: 0.617\n",
      "[89,  6000] loss: 0.612\n",
      "[89,  8000] loss: 0.715\n",
      "[89, 10000] loss: 0.658\n",
      "[89, 12000] loss: 0.690\n",
      "[90,  2000] loss: 0.591\n",
      "[90,  4000] loss: 0.682\n",
      "[90,  6000] loss: 0.667\n",
      "[90,  8000] loss: 0.670\n",
      "[90, 10000] loss: 0.638\n",
      "[90, 12000] loss: 0.704\n",
      "[91,  2000] loss: 0.606\n",
      "[91,  4000] loss: 0.687\n",
      "[91,  6000] loss: 0.647\n",
      "[91,  8000] loss: 0.687\n",
      "[91, 10000] loss: 0.702\n",
      "[91, 12000] loss: 0.729\n",
      "[92,  2000] loss: 0.578\n",
      "[92,  4000] loss: 0.612\n",
      "[92,  6000] loss: 0.696\n",
      "[92,  8000] loss: 0.667\n",
      "[92, 10000] loss: 0.695\n",
      "[92, 12000] loss: 0.664\n",
      "[93,  2000] loss: 0.573\n",
      "[93,  4000] loss: 0.597\n",
      "[93,  6000] loss: 0.718\n",
      "[93,  8000] loss: 0.708\n",
      "[93, 10000] loss: 0.695\n",
      "[93, 12000] loss: 0.695\n",
      "[94,  2000] loss: 0.625\n",
      "[94,  4000] loss: 0.665\n",
      "[94,  6000] loss: 0.668\n",
      "[94,  8000] loss: 0.690\n",
      "[94, 10000] loss: 0.691\n",
      "[94, 12000] loss: 0.663\n",
      "[95,  2000] loss: 0.612\n",
      "[95,  4000] loss: 0.638\n",
      "[95,  6000] loss: 0.676\n",
      "[95,  8000] loss: 0.693\n",
      "[95, 10000] loss: 0.706\n",
      "[95, 12000] loss: 0.715\n",
      "[96,  2000] loss: 0.650\n",
      "[96,  4000] loss: 0.688\n",
      "[96,  6000] loss: 0.687\n",
      "[96,  8000] loss: 0.692\n",
      "[96, 10000] loss: 0.747\n",
      "[96, 12000] loss: 0.676\n",
      "[97,  2000] loss: 0.586\n",
      "[97,  4000] loss: 0.666\n",
      "[97,  6000] loss: 0.648\n",
      "[97,  8000] loss: 0.735\n",
      "[97, 10000] loss: 0.676\n",
      "[97, 12000] loss: 0.698\n",
      "[98,  2000] loss: 0.596\n",
      "[98,  4000] loss: 0.637\n",
      "[98,  6000] loss: 0.721\n",
      "[98,  8000] loss: 0.748\n",
      "[98, 10000] loss: 0.700\n",
      "[98, 12000] loss: 0.788\n",
      "[99,  2000] loss: 0.609\n",
      "[99,  4000] loss: 0.639\n",
      "[99,  6000] loss: 0.678\n",
      "[99,  8000] loss: 0.728\n",
      "[99, 10000] loss: 0.694\n",
      "[99, 12000] loss: 0.735\n",
      "[100,  2000] loss: 0.637\n",
      "[100,  4000] loss: 0.645\n",
      "[100,  6000] loss: 0.699\n",
      "[100,  8000] loss: 0.708\n",
      "[100, 10000] loss: 0.779\n",
      "[100, 12000] loss: 0.755\n",
      "Finished Training\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "dataiter  = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "imshow(torchvision.utils.make_grid((images)))\n",
    "print(\"GT \", ' '.join('%5s' % classes[labels[j]] for j in range(4)))\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "GT    cat  ship  ship plane\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "source": [
    "outputs = net(images.cuda())"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "source": [
    "_, predicted = torch.max(outputs, 1)\n",
    "print('Predicted: ', ' '.join('%5s'% classes[predicted[j]] for j in range(4)))\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Predicted:    dog plane truck  ship\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        images = images.cuda()\n",
    "        labels = labels.cuda()\n",
    "        outputs = net(images)\n",
    "        _, predicted = torch.max(outputs.data,1)\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted==labels).sum().item()\n",
    "\n",
    "print(\"Accuracy of the network on the 10000 test images: %d %%\" % (100*correct/total))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Accuracy of the network on the 10000 test images: 55 %\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "source": [
    "class_correct = list(0. for i in range(10))\n",
    "class_total = list(0. for i in range(10))\n",
    "with torch.no_grad():\n",
    "    for data in testloader:\n",
    "        images, labels = data\n",
    "        images = images.cuda()\n",
    "        labels = labels.cuda()\n",
    "        outputs = net(images)\n",
    "        _, predicted = torch.max(outputs,1)\n",
    "        c = (predicted==labels).squeeze()\n",
    "        for i in range(4):\n",
    "            label = labels[i]\n",
    "            class_correct[label] += c[i].item()\n",
    "            class_total[label] += 1\n",
    "\n",
    "for i in range(10):\n",
    "    print('Accuracy of %5s : %2d %%' % \n",
    "                    (classes[i], 100*class_correct[i]/class_total[i]))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Accuracy of plane : 46 %\n",
      "Accuracy of   car : 67 %\n",
      "Accuracy of  bird : 55 %\n",
      "Accuracy of   cat : 45 %\n",
      "Accuracy of  deer : 48 %\n",
      "Accuracy of   dog : 41 %\n",
      "Accuracy of  frog : 58 %\n",
      "Accuracy of horse : 59 %\n",
      "Accuracy of  ship : 76 %\n",
      "Accuracy of truck : 56 %\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "source": [
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "![](2021-10-15-18-59-24.png)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
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  "language_info": {
   "name": "python",
   "version": "3.8.10",
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
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  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.10 64-bit ('d2l-zh': conda)"
  },
  "interpreter": {
   "hash": "0bedf3452ed48e38a2b75ff9f66ce8ae9ae2e92e8665cb618ee0797d2f46b9e5"
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 },
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